Cloud-RSA: An Enhanced Homomorphic Encryption Scheme

With the enormous use of cloud technology, sensitive information is being stored on cloud. This has raised the issue of confidentiality and integrity of data. In traditional approach by encrypting the data before outsourcing we can achieve data confidentiality. Although, encryption provides security, but this also create problem in data updation and search in the encrypted form. For data updation, either we have to share decryption key with service provider or we have to download and upload the data again. In this paper authors have provided the solution to the above stated problems. Authors have analyzed the various public key cryptosystem followed by their homomorphic properties. Using homomorphic encryption technique, authors have proposed a security model, which enhances the data confidentiality and integrity.

Homomorphic Encryption has become a viable method for privacy preservation. It basically deals with the computations of encoded data. Homomorphic encryption scheme allows operations on encrypted data which, when decrypted will provide the same results as we performed directly on the raw data. Certain cryptographic algorithms having the homomorphic property. A detailed survey is carried out on the various homomorphic encryption scheme based on the parameter involved, the encryption decryption mechanisms, their hammerphic properties, security considerations etc. The encryption methods with homomorphic property are much suitable for applications require data privacy and security.

This chapter discusses two popular homomorphic public key encryption (PKE) techniques, i.e., Paillier PKE and Boneh-Goh-Nissim (BGN) PKE, which serve as the preliminary for building most privacy-enhancing aggregation techniques in the rest chapters of this monograph. In addition, the Java source codes of the two homomorphic PKEs are also provided for the interested readers to better understand and implement them. In order to attain privacy-preserving data aggregation in smart grid communications, we need to first understand the homomorphic public key encryption (HPKE) techniques [1, 2, 3, 4, 5, 6, 7]. Different from the general public key encryption algorithms [8, 9], HPKE further holds an additional “homomorphic” property, which makes the privacy-preserving data aggregation possible. As shown in Fig. 2.1, when we directly operate over two encrypted data E(x) and E(y) with some operation “•”, we can gain E(x ∘ y) = E(x)•E(y). In most HPKE cases, the operations “•” and “∘” are respectively referred to the common multiplication “×” and addition “+” operations. Because most of privacy-enhancing aggregation techniques illustrated in this monograph are based on Paillier public key encryption [2] and Boneh-Goh-Nissim (BGN) public key encryption [6], in this chapter, we first take a close look at these two popular homomorphic encryption techniques. Note that, both of them are randomized encryption algorithms [1], i.e., in addition to the plaintext as the input of encryption, a random number is also input for achieving semantic security.

In cryptography, there has been tremendous success in building various two-party protocols with small communication complexity out of homomorphic semantically-secure encryption schemes, using their homomorphic properties in a black-box way. A few notable examples of such primitives include items like single database Private Information Retrieval (PIR) schemes (introduced in [15]) and private database update with small communication (introduced in [5]). In this paper, we illustrate a general methodology for determining what types of protocols can and cannot be implemented with small communication by using homomorphic encryption in a black-box way. We hope that this work will provide a simple litmus test of feasibility for black-box use of known homomorphic encryption schemes by other cryptographic researchers attempting to develop new protocols with low communication. Additionally, a precise mathematical language for reasoning about such problems is developed in this work, which may be of independent interest. We stress that the class of algebraic structures for which we prove communication complexity lower bounds is large, and covers practically all known semantically-secure homomorphic cryptosystems (including those based upon bilinear maps). Finally, we show the following equivalence which relates group homomorphic encryption and a major open question of designing a so-called fully-homomorphic cryptosystem: a fully homomorphic encryption scheme (over a non-zero ring) exists if and only if there exists homomorphic encryption over any finite non-abelian simple group. This result somewhat generalizes results of Barrington [1] (to any group containing a finite non-abelian simple subgroup) and of Maurer and Rhodes [18], and in fact gives a constructiveproof of the 1974 result Werner [28]. (This also answers an open question posed by Rappe in [23], who in 2004 proved a special case of this result.)

In homomorphic encryption schemes, anyone can perform homomorphic operations, and therefore, it is difficult to manage when, where and by whom they are performed. In addition, the property that anyone can ‘‘freely” perform the operation inevitably means that ciphertexts are malleable, and it is well-known that adaptive chosen ciphertext (CCA) security and the homomorphic property can never be achieved simultaneously. In this paper, we show that CCA security and the homomorphic property can be simultaneously handled in situations that the user(s) who can perform homomorphic operations on encrypted data should be controlled/limited, and propose a new concept of homomorphic public-key encryption, which we call keyed-homomorphic public-key encryption (KH-PKE). By introducing a secret key for homomorphic operations, we can control who is allowed to perform the homomorphic operation. To construct KH-PKE schemes, we introduce a new concept, a homomorphic transitional universal hash family, and present a number of KH-PKE schemes through hash proof systems. We also present a practical construction of KH-PKE from the DDH assumption. For ℓ-bit security, our DDH-based scheme yields only ℓ-bit longer ciphertext size than that of the Cramer-Shoup PKE scheme.Keywordshomomorphic public key encryptionCCA2 securityhash proof system

Machine learning servers with mass storage and computing power is an ideal platform to store, manage, and analyze data and support decision-making. However, the main issue is providing security and privacy to the data, as the data is stored in a public way. Recently, homomorphic data encryption has been proposed as a solution due to its capabilities in performing computations over encrypted data. In this paper, we proposed an encrypted all convolutional net that transformed traditional all convolutional net into a net based on homomorphic encryption. This scheme allows different data holders to send their encrypted data to cloud service, complete predictions, and return them in encrypted form as the cloud service provider does not have a secret key. Therefore, the cloud service provider and others cannot get unencrypted raw data. When applied to the MNIST database, privacy-preserving all convolutional based on homomorphic encryption predict efficiently, accurately and with privacy protection.

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

The search for encryption schemes that allow to evaluate functions (or circuits) over encrypted data has attracted a lot of attention since the seminal work on this subject by Rivest, Adleman and Dertouzos in 1978. In this work we define a theoretical object, chained encryption schemes, which allow an efficient evaluation of polynomials of degree d over encrypted data. Chained encryption schemes are generically constructed by concatenating cryptosystems with the appropriate homomorphic properties; such schemes are common in lattice-based cryptography. As a particular instantiation we propose a chained encryption scheme whose INDCPA security is based on a worst-case/average-case reduction from uSVP.

In the Database-As-a-Service (DAS) model, clients store their database contents at servers belonging to potentially untrusted service providers. To maintain data confidentiality, clients need to outsource their data to servers in encrypted form. At the same time, clients must still be able to execute queries over encrypted data. One prominent and fairly effective technique for executing SQL-style range queries over encrypted data involves partitioning (or bucketization) of encrypted attributes.However, executing aggregation-type queries over encrypted data is a notoriously difficult problem. One well-known cryptographic tool often utilized to support encrypted aggregation is homomorphic encryption; it enables arithmetic operations over encrypted data. One technique based on a specific homomorphic encryption function was recently proposed in the context of the DAS model. Unfortunately, as shown in this paper, this technique is insecure against ciphertext-only attacks. We propose a simple alternative for handling encrypted aggregation queries and describe its implementation. We also consider a different flavor of the DAS model which involves mixed databases, where some attributes are encrypted and some are left in the clear. We show how range queries can be executed in this model.

An encryption scheme is homomorphic if it supports operations on encrypted data. Homomorphic encryption allows a device to perform arbitrary computations on encrypted data without user secret key. Recently it is introduced new homomorphic encryption schemes with improved performance that can be implemented in IoT device in production environments. The IoT concept encompasses devices, sensors, and services existing within an interconnected infrastructure with an efficient access to sample computational facilities. In this paper we evaluated features of exact arithmetic homomorphic encryption mechanisms: BFV and BGV and approximate homomorphic encryption scheme: CKKS. In the paper we measured performances of operations of homomorphic encryption schemes: BGV, BFV and CKKS that are implemented in Raspberry Pi 4 IoT device.

This paper discusses how to implement Paillier homomorphic encryption (HE) scheme in Java as an API. We first analyze existing Pailler HE libraries and discuss their limitations. We then design a comparatively accomplished and efficient Pailler HE Java library. As a proof of concept, we applied our Pailler HE library in an electronic voting system that allows the voting server to sum up the candidates' votes in the encrypted form with voters remain anonymous. Our library records an average of only 2766ms for each vote placement through HTTP POST request.

The protection and processing of sensitive data in big data systems are common problems as the increase in data size increases the need for high processing power. Protection of the sensitive data on a system that contains multiple connections with different privacy policies, also brings the need to use proper cryptographic key exchange methods for each party, as extra work. Homomorphic encryption methods can perform similar arithmetic operations on encrypted data in the same way as a plain format of the data. Thus, these methods provide data privacy, as data are processed in the encrypted domain, without the need for a plain form and this allows outsourcing of the computations to cloud systems. This also brings simplicity on key exchange sessions for all sides. In this paper, we propose novel privacy preserving clustering methods, alongside homomorphic encryption schemes that can run on a common high performance computation platform, such as a cloud system. As a result, the parties of this system will not need to possess high processing power because the most power demanding tasks would be done on any cloud system provider. Our system offers a privacy preserving distance matrix calculation for several clustering algorithms. Considering both encrypted and plain forms of the same data for different key and data lengths, our privacy preserving training method’s performance results are obtained for four different data clustering algorithms, while considering six different evaluation metrics.

In this paper, privacy preserving at the cloud side is enabled by adopting Homomorphic Encryption (HE) as a major solution for protecting users' sensitive data. As a Proof Of Concept (POC), we take the case of a university that is adopting the cloud as a practical method for storing and operating over its private data. The main contribution of this application is that different data are stored encrypted at the cloud side. Students' data such as name, gender, date of birth, etc are encrypted using AES and hash functions. Students' indexes and grades are encrypted homomorphically using our modified Domingo Ferrer (DF) encryption scheme [1]. The main importance of the usage of HE in this application is allowing the process over encrypted data at the cloud side such as computing encrypted students' average. Different queries are sent from the university side to the cloud side, after operating and processing over encrypted data all results are shipped back encrypted to the university where the primitive data is recovered. Cloud infrastructure is created using Apache CloudStack and different encryption schemes are implemented under Python using SageMath library.

Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.